The respiration pattern of a patient can be used to optimize particle beam radiation therapy. The intent is estimate the pattern that corresponds to “breathing in” and “breathing out.” The estimation should be dynamic and real time to control the energy of the particle beam so that a maximal dosage is delivered to any abnormal tissue, while the dosage to normal tissue is minimized.
Instead of the respiration information, a change pattern of the depicted target tissue can also employed to achieve the adaptive gating of such therapy systems. For instance, the current state e.g. ultrasound image of the tumor is compared to the safe radiation state i.e. the ultrasound image where the tumor is in the desired position. In case these states match the radiation is applied. However, a change in these states indicates the tumor may not be in the desired location, thus the radiation should not be applied.
An evaluation of the accuracy of the estimation is also important, although not straightforward. In theory, if the exact signal is available, then statistical quantities, such as Euclidean distance, cross-correlation or phase-correlation between the exact signal and the estimated pattern, can be determined. However, the exact signal is not available during the treatment.
As a result, the only way to evaluate the change pattern is by visualization. As the change pattern is estimated from an ultrasonic video, the signal pattern should be visually compared with the video to measure the correlation. This visualization should reveal whether the expected pattern of “breathing” in the video matches the estimated pattern, or whether the pattern is deviant in case of respiration.
That is, radiotherapy technicians need an effective visualization to detect any deviant change in the respiration pattern during the treatment, and make a decision whether the therapy should be continued or terminated.
It is also desired to provide an effective visualization to determine whether the phases and frequencies of the estimated change pattern match the motion of organs as seen in the video. Given a 2D video and a change pattern signal, the goal is to highlight the periodicity of the underline motion in the video, and efficiently comparing the correlation between the motion in the 2D video and the 1D signal. However, a long video is time-consuming to watch.
Video surveillance applications can visualize events by representing the video as a static spatio-temporal volume. After low level image features, such as gradients or motion flows, have been extracted from the volume, convectional volume rendering techniques used to enhance those features to visualize the underline events. For example, direct volume rendering, with transfer functions that assign high transparencies to static region can hide the background in a scene, and flow visualization techniques such as glyphs or streamlines integrals can be applied to visualize the extracted motion flow. The common goal in those techniques is visualizing the motion in the environment.
If the ultrasonic video is watched as conventional animation, then the user cannot precisely reveal the periodicity, especially the duration of cycle in the pattern and the shift of phase that are common in medical imaging, because the phases and frequencies of respiration are dynamic. It is also difficult to check the correlation between the moving 2D pattern and the 1D signal over longer time intervals. For instance, the video and the signal can be positively correlated in one part of the video, but negatively correlated in another part. The user cannot immediately recall and detect such a difference when the phase of the correlation is continuously delayed.
Conventional video representations utilize a 2D display screen as the X, Y coordinate space, and changes the image (frames) over time, while the plotting of a 1D signal y=f(t) often utilizes the 2D display screen as the T, Y coordinate space. This difference makes it non-intuitive to compare the 2D spatial video and the 1D temporal signal pattern, especially when the video frame is changing. If a video with thousands of frames is represented as a long volume, then only a part of the video can be displayed on the screen at any time in the Y, T coordinate space.
Therefore, it is desired concurrently visualize the 2D video and the 1D change signal.